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CCNA AI Capabilities in CRM Questions

43 questions · AI Capabilities in CRM · All types, answers revealed

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1

MCQhard

A financial services company uses Salesforce Service Cloud with Einstein Bots to handle account balance inquiries. The bot currently uses a standard intent 'CheckBalance' which recognizes phrases like 'What is my balance?' and 'Show my account balance.' The company wants to expand the bot to also answer questions about recent transactions, such as 'What were my last five deposits?' and 'Show my recent withdrawals.' The system administrator has added a new intent called 'RecentTransactions' and mapped it to a new flow. However, during testing, the bot often misclassifies 'CheckBalance' requests as 'RecentTransactions' when the user mentions a specific amount or date. Which action should the administrator take to resolve this misclassification?

A.Add sample utterances containing amounts and date ranges to the 'CheckBalance' intent to differentiate it.

B.Reduce the confidence threshold for both intents to allow more matches.

C.Disable the 'RecentTransactions' intent and handle transaction requests using a flow without intents.

D.Create a new Einstein Bot specifically for transaction inquiries and route users there.

AnswerA

Providing more training data for the existing intent helps the model distinguish between similar phrases.

Why this answer

Adding sample utterances that include amounts and date ranges to the 'CheckBalance' intent provides the Einstein Bot's natural language processing (NLP) model with more training data to distinguish between balance inquiries and transaction requests. This improves intent classification accuracy by reducing overlap in the phrases the bot recognizes, directly addressing the misclassification issue.

Exam trap

The trap here is that candidates may think lowering the confidence threshold or creating separate bots will fix misclassification, but the correct approach is to enrich the training data for the existing intents to improve the NLP model's accuracy.

How to eliminate wrong answers

Option B is wrong because reducing the confidence threshold would cause the bot to match intents more loosely, likely increasing misclassifications rather than resolving them. Option C is wrong because disabling the 'RecentTransactions' intent would prevent the bot from handling transaction inquiries at all, which contradicts the expansion goal. Option D is wrong because creating a separate bot for transactions adds unnecessary complexity and does not fix the root cause of intent confusion; the same misclassification could occur if users are routed incorrectly.

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2

MCQeasy

A marketing manager wants to prioritize leads with the highest likelihood of conversion. Which Einstein feature should they use?

A.Einstein Lead Scoring

B.Approval Processes

C.Custom Formula Fields

D.Data Export

AnswerA

Automatically scores leads based on historical data.

Why this answer

Einstein Lead Scoring predicts conversion probability for each lead. Custom formulas are manual, approval processes and data export are not predictive.

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3

Multi-Selectmedium

Which THREE are requirements for enabling Einstein features in a Salesforce org?

Select 3 answers

A.An eligible Salesforce edition

B.Custom objects must be created

C.User permissions to view predictions

D.Activation of Einstein API in Setup

E.A minimum threshold of relevant data

AnswersA, C, E

Only certain editions support Einstein.

Why this answer

Einstein features require an eligible Salesforce edition (e.g., Enterprise, Performance, or Unlimited) because the underlying AI infrastructure, including predictive models and data processing pipelines, is only available in these higher-tier editions. Without the correct edition, the necessary licenses and platform capabilities for Einstein are not provisioned.

Exam trap

The trap here is that candidates often confuse 'enabling Einstein features' with 'configuring Einstein API access,' but Salesforce does not expose a standalone API toggle for Einstein; instead, edition eligibility and data thresholds are the foundational requirements.

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4

MCQmedium

A sales rep wants to generate personalized email drafts for leads using AI. Which feature should the admin enable?

A.Workflow Rules

B.Einstein GPT

C.Process Builder

D.Email Templates

AnswerB

Einstein GPT generates AI-powered drafts from prompts.

Why this answer

Einstein GPT is the correct feature because it is Salesforce's native generative AI tool that can automatically create personalized email drafts for leads by leveraging CRM data and natural language processing. Unlike other options, Einstein GPT is specifically designed for AI-driven content generation within the Salesforce ecosystem.

Exam trap

Salesforce often tests the distinction between traditional automation tools (Workflow Rules, Process Builder) and AI-powered content generation (Einstein GPT), leading candidates to mistakenly choose a familiar automation feature instead of the correct AI-specific one.

How to eliminate wrong answers

Option A is wrong because Workflow Rules are a declarative automation tool for triggering actions based on record changes, not for generating AI-based content. Option C is wrong because Process Builder is a point-and-click automation tool for creating complex business processes, not for generating personalized email drafts using AI. Option D is wrong because Email Templates are static, reusable message formats that require manual selection and do not use AI to dynamically generate personalized content for each lead.

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5

MCQeasy

To recommend the most relevant action for a service agent during a call, which feature should be used?

A.Flow

B.Einstein Next Best Action

C.Quick Actions

D.Process Builder

AnswerB

Provides AI-driven recommendations in real time.

Why this answer

Einstein Next Best Action is the correct feature because it uses AI to analyze the call context in real time and recommend the most relevant action for a service agent, such as offering a specific discount or knowledge article. Unlike static automation tools, it leverages predictive models to adapt recommendations based on customer data and conversation sentiment, ensuring the agent takes the optimal next step.

Exam trap

Salesforce often tests the misconception that any automation tool (like Flow or Process Builder) can provide AI-driven recommendations, but only Einstein Next Best Action is purpose-built for real-time, context-aware suggestions during a service call.

How to eliminate wrong answers

Option A is wrong because Flow is a declarative automation tool for building guided processes or screen flows, but it does not use AI to dynamically recommend actions based on real-time call context. Option C is wrong because Quick Actions are predefined, one-click actions (e.g., log a call, create a task) that are static and not AI-driven, so they cannot recommend the most relevant action during a call. Option D is wrong because Process Builder is a point-and-click tool for automating standard business processes (e.g., record updates, email alerts) and lacks AI capabilities to generate context-aware recommendations.

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6

MCQmedium

Refer to the exhibit. A Salesforce admin sees this error when trying to enable Einstein Lead Scoring. What should the admin do to resolve the issue?

A.Enable Einstein features in the org

B.Map lead fields to Einstein fields

C.Add more lead records with associated activities until reaching at least 100

D.Grant the admin the 'Manage Einstein' permission

AnswerC

The model needs 100 leads with activities to train.

Why this answer

Option C is correct because Einstein Lead Scoring requires a minimum of 100 lead records with associated activities (e.g., emails, events, tasks) to generate a predictive model. The error indicates insufficient data, so adding more leads with activities meets the threshold for model training.

Exam trap

Salesforce often tests the minimum data requirement (100 leads with activities) as a common pitfall, where candidates mistakenly focus on permissions or feature toggles instead of the data prerequisite.

How to eliminate wrong answers

Option A is wrong because the error is not about enabling Einstein features globally; the admin already attempted to enable scoring, implying features are enabled. Option B is wrong because lead field mapping is not required for Einstein Lead Scoring; the feature uses standard lead fields automatically. Option D is wrong because the 'Manage Einstein' permission is not a prerequisite for enabling scoring; the admin likely already has necessary permissions if they can access the setup page.

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7

MCQeasy

A service manager wants to automatically categorize incoming support cases based on the customer's description. Which Einstein feature should be used?

A.Einstein Reply Recommendations

B.Einstein Case Classification

C.Einstein Bots

D.Einstein Article Recommendations

AnswerB

Case Classification automatically categorizes cases.

Why this answer

Einstein Case Classification uses machine learning to automatically categorize incoming support cases based on the customer's description, assigning them to predefined case fields (e.g., type, priority, product). This directly matches the requirement to automatically categorize cases from text, making it the correct choice.

Exam trap

Salesforce often tests the distinction between 'categorization' and 'recommendation' features, so the trap here is confusing Einstein Case Classification (which assigns labels to the case) with Einstein Article Recommendations (which suggests content to the user).

How to eliminate wrong answers

Option A is wrong because Einstein Reply Recommendations suggests pre-written email responses based on context, not categorization of cases. Option C is wrong because Einstein Bots automate conversational flows and deflect cases, but they do not perform classification of case records. Option D is wrong because Einstein Article Recommendations suggests knowledge articles to agents or customers, not categorizing the case itself.

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8

MCQmedium

An admin is setting up Einstein Bot for a customer service chat. The bot needs to collect the customer's account number before transferring to a human agent. What should the admin configure?

A.Add a Variable with the account number field

B.Create a Dialog to ask for the account number

C.Define a Rule to validate the account number

D.Use a Set Value action to assign the account number

AnswerB

Dialogs guide the conversation and collect input.

Why this answer

Option B is correct because a Dialog is the component that handles a conversation flow, including collecting information. Option A is incorrect because a Variable stores data but does not collect it. Option C is incorrect because a Rule determines logic, not collection.

Option D is incorrect because a Set Value action sets a variable, but the overall collection happens within a Dialog.

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9

MCQeasy

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should be used?

A.Einstein Prediction Builder

B.Einstein Relationship Health

C.Einstein Lead Scoring

D.Einstein Activity Capture

AnswerC

Einstein Lead Scoring prioritizes leads based on conversion likelihood.

Why this answer

Einstein Lead Scoring is the correct feature because it uses predictive models to automatically rank leads based on their likelihood to convert, enabling the sales manager to prioritize follow-up efforts. It analyzes historical lead data and engagement patterns to assign a score, directly addressing the requirement for automated prioritization without manual rules.

Exam trap

Salesforce often tests the distinction between Einstein Lead Scoring and Einstein Prediction Builder, trapping candidates who think any predictive model builder can be used for lead scoring, when in fact Lead Scoring is a purpose-built feature for that exact use case.

How to eliminate wrong answers

Option A is wrong because Einstein Prediction Builder is a no-code tool for creating custom predictive models on any object or field, not specifically designed for lead scoring or conversion likelihood. Option B is wrong because Einstein Relationship Health measures the strength of existing account relationships, not the conversion potential of new leads. Option D is wrong because Einstein Activity Capture automatically logs emails and events to Salesforce records, but it does not perform any predictive scoring or prioritization of leads.

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10

MCQmedium

A company uses Salesforce and wants to provide automated chat responses for common customer inquiries. Which Einstein feature should be configured?

A.Einstein Sentiment

B.Einstein Case Routing

C.Einstein Bots

D.Einstein Recommender

AnswerC

Einstein Bots provide automated chat responses.

Why this answer

Einstein Bots is the correct feature because it enables automated chat responses for common customer inquiries using natural language processing (NLP) and predefined dialog flows. It integrates directly with Salesforce Chat to handle routine questions without human intervention, reducing response times and agent workload.

Exam trap

Salesforce often tests the distinction between features that analyze data (Sentiment, Recommender) versus those that automate actions (Bots), leading candidates to confuse Einstein Sentiment or Recommender as capable of providing chat responses when they are purely analytical or recommendation tools.

How to eliminate wrong answers

Option A is wrong because Einstein Sentiment analyzes the emotional tone of text (e.g., positive, negative, neutral) in conversations or social posts, not for providing automated chat responses. Option B is wrong because Einstein Case Routing uses machine learning to assign cases to the best agent based on skills and availability, not to generate chat replies. Option D is wrong because Einstein Recommender suggests next-best actions or products to agents or customers based on historical data, not for automating chat responses.

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11

MCQmedium

A company wants to use Einstein to predict which customers are likely to churn in the next 30 days. Which type of prediction should be created in Einstein Prediction Builder?

A.Numeric prediction

B.Multi-class classification

C.Regression

D.Binary classification

AnswerD

Binary classification predicts one of two outcomes, like churn or not churn.

Why this answer

Einstein Prediction Builder is designed for binary outcomes, such as whether a customer will churn (yes/no) within a specified time frame. A binary classification model predicts one of two possible labels, making it the correct choice for this churn prediction use case.

Exam trap

Salesforce often tests the distinction between regression (numeric prediction) and classification (categorical prediction), and candidates mistakenly choose 'Numeric prediction' or 'Regression' because churn prediction involves a time-based numeric threshold (30 days), but the output is still a binary label, not a number.

How to eliminate wrong answers

Option A is wrong because numeric prediction is used for forecasting a continuous numerical value (e.g., revenue amount), not a categorical outcome like churn. Option B is wrong because multi-class classification predicts among three or more categories (e.g., product type), whereas churn is a binary yes/no outcome. Option C is wrong because regression is a type of numeric prediction for continuous values, not suitable for a binary classification task.

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12

MCQeasy

To identify common customer issues from chat transcripts using AI, which feature should be used?

A.Einstein Conversation Mining

B.Post-Interaction Survey

C.Reports

D.Dashboards

AnswerA

Extracts insights from conversation data using NLP.

Why this answer

Einstein Conversation Mining analyzes unstructured chat data to surface common themes. Surveys collect feedback, reports and dashboards show aggregated data but not insights from text.

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13

Multi-Selecthard

Which TWO are best practices when implementing Einstein Bots? (Choose two.)

Select 2 answers

A.Start with high-volume, low-complexity conversations

B.Use complex intents for initial setup

C.Configure the bot to handle all customer conversations

D.Continuously improve intents based on conversation logs

E.Deploy the bot to all channels immediately without testing

AnswersA, D

Ensures easy wins and learning.

Why this answer

Option A is correct because Einstein Bots are designed to automate high-volume, low-complexity conversations first, such as password resets or order status inquiries. This approach allows the bot to handle the most frequent interactions efficiently, reducing agent workload and providing immediate ROI. Starting simple also enables easier intent training and faster deployment, aligning with best practices for conversational AI implementation.

Exam trap

Salesforce often tests the misconception that bots should handle everything immediately, but the best practice is to start small and iterate based on conversation logs to improve intent accuracy and coverage.

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14

MCQhard

A company has a custom AI model for sentiment analysis and wants to use it in Salesforce without rebuilding. Which approach should they take?

A.Use Data Export and import into external system

B.Use Bring Your Own Model (BYOM) for Einstein

C.Use MuleSoft

D.Build in Apex

AnswerB

Enables custom model deployment in Salesforce.

Why this answer

Bring Your Own Model (BYOM) for Einstein allows companies to deploy their own pre-trained AI models directly into Salesforce without rebuilding them. This approach leverages Salesforce's infrastructure for inference while keeping the custom model intact, making it the ideal solution for integrating a custom sentiment analysis model.

Exam trap

The trap here is that candidates may confuse MuleSoft (an integration tool) with a model deployment service, or assume that any external model must be rebuilt in Apex, when BYOM is specifically designed to avoid that.

How to eliminate wrong answers

Option A is wrong because Data Export is a tool for exporting Salesforce data to external systems, not for importing or running custom AI models within Salesforce. Option C is wrong because MuleSoft is an integration platform for connecting applications and data, not a service for deploying custom AI models into Salesforce's AI framework. Option D is wrong because building the model in Apex would require rewriting the entire model from scratch in Apex code, which is impractical for complex machine learning models and defeats the purpose of using an existing custom model.

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15

MCQhard

A legal firm wants to automate contract clause generation using AI with preapproved language. Which approach should they use?

A.Email Templates

B.Flow

C.Document Templates

D.Einstein GPT with Clause Library

AnswerD

Generates AI text based on approved clauses.

Why this answer

Option D is correct because Einstein GPT with Clause Library is specifically designed to generate contract clauses using preapproved language. It leverages generative AI combined with a library of approved legal clauses, ensuring compliance and consistency while automating clause creation within Salesforce.

Exam trap

The trap here is that candidates confuse Document Templates (static, pre-filled forms) with AI-powered clause generation, not realizing that Einstein GPT with Clause Library dynamically selects and inserts preapproved language, whereas Document Templates require manual clause insertion or simple merge fields.

How to eliminate wrong answers

Option A is wrong because Email Templates are used for standardizing email communications, not for generating contract clauses with preapproved language. Option B is wrong because Flow is a process automation tool for orchestrating actions and approvals, not a content generation system for legal clauses. Option C is wrong because Document Templates provide static document structures but lack the AI-driven clause selection and generation capabilities needed for dynamic, preapproved clause insertion.

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16

MCQmedium

A company wants an AI chatbot that can handle customer inquiries about order status. Which tool should be configured?

A.Queues

B.Einstein Bot

C.Omni-Channel

D.Case Assignment Rules

AnswerB

Provides AI-powered chat for automated responses.

Why this answer

Einstein Bot is the correct tool because it is Salesforce's native AI-powered chatbot designed to handle customer inquiries, including order status, through natural language processing and automated conversations. It can be configured to answer common questions, escalate complex issues, and integrate with backend systems to retrieve real-time order data without human intervention.

Exam trap

The trap here is that candidates often confuse routing tools (Omni-Channel, Queues) or automation rules (Case Assignment Rules) with AI-powered conversational tools, mistakenly thinking any routing or assignment feature can handle customer inquiries directly.

How to eliminate wrong answers

Option A is wrong because Queues are used for routing work items (like cases or leads) to a group of users based on assignment rules, not for building conversational AI or handling customer inquiries directly. Option C is wrong because Omni-Channel is a routing engine that distributes work across channels (chat, phone, etc.) to available agents, but it does not provide AI-driven chatbot capabilities or automated responses. Option D is wrong because Case Assignment Rules automatically assign cases to users or queues based on criteria, but they lack the conversational AI and natural language understanding needed to interact with customers and answer order status questions.

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17

Multi-Selectmedium

Which TWO of the following are limitations of Einstein GPT? (Choose two.)

Select 2 answers

A.It requires structured prompts for best results

B.It can automatically generate account summaries from leads

C.It may produce biased or inaccurate content

D.It supports all languages equally

E.It requires no training data

AnswersA, C

Prompts must be well-formatted.

Why this answer

Einstein GPT relies on structured prompts to guide the generative AI model toward relevant and accurate outputs. Without clear, well-formed prompts, the model may produce vague or off-target responses, making prompt engineering a critical skill for users.

Exam trap

Salesforce often tests the misconception that generative AI tools like Einstein GPT are fully autonomous or require no user input, when in reality they depend on structured prompts and quality training data for reliable results.

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18

MCQmedium

A retail company wants to increase average order value by showing personalized product recommendations on their website. They currently use Salesforce Commerce Cloud and have Einstein Recommendations enabled. However, they notice that recommendations are not reflecting recent customer interactions, such as items added to cart but not purchased. What should the administrator do to improve recommendation relevance?

A.Reset the Einstein Recommendations model and retrain from scratch.

B.Implement an Einstein Bot to directly ask customers about their preferences.

C.Disable Einstein Recommendations for email and ads, leaving only web recommendations.

D.Enable the 'Add to Cart' and 'Checkout' events in the Einstein Recommendations data integration to capture real-time data.

AnswerD

Capturing real-time events allows the model to incorporate recent cart activity.

Why this answer

Option D is correct because Einstein Recommendations relies on data integration events to capture real-time customer behavior. By enabling 'Add to Cart' and 'Checkout' events, the system can ingest recent interactions (e.g., items added to cart but not purchased) and adjust recommendations accordingly, improving relevance without requiring a full model reset.

Exam trap

Salesforce often tests the misconception that resetting the model (Option A) is the default fix for stale recommendations, when in fact the root cause is almost always missing or misconfigured data integration events.

How to eliminate wrong answers

Option A is wrong because resetting and retraining the model from scratch would discard all historical learning and does not address the missing real-time event data; it is an overreaction that would degrade performance temporarily. Option B is wrong because an Einstein Bot is designed for conversational interactions, not for passively capturing behavioral events like add-to-cart; it would add unnecessary complexity and user friction without solving the data integration gap. Option C is wrong because disabling recommendations for email and ads does not affect the capture of real-time events on the website; the issue is about data ingestion, not channel distribution.

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19

MCQmedium

A nonprofit organization uses Salesforce Nonprofit Cloud with Einstein Discovery to analyze donation patterns. They have activated a story that predicts which donors are most likely to churn (stop donating) in the next three months. The story shows a top influence called 'DonationFrequency' with a negative correlation: donors who donate less than once per quarter are 40% more likely to churn. The director of development wants to use this insight to create a retention campaign. However, the story also includes a field called 'LastDonationAmount' which has a small positive influence. The development team wants to ensure the predictions are actionable. What should the administrator do to maximize the effectiveness of the Einstein Discovery story for this retention campaign?

A.Retrain the prediction model using only 'DonationFrequency' and 'LastDonationAmount' as predictors.

B.Delete the 'LastDonationAmount' influence from the story to simplify the output.

C.Adjust the influence weight of 'DonationFrequency' to be higher in the story settings.

D.Create a segment of donors with low donation frequency and use that as the target for the retention campaign.

AnswerD

Focusing on the strongest actionable influence maximizes campaign impact.

Why this answer

Option D is correct because the most actionable insight from the Einstein Discovery story is the strong negative correlation of 'DonationFrequency' with churn. By creating a segment of donors with low donation frequency, the administrator can directly target the highest-risk group for a retention campaign, making the prediction actionable without altering the model or its output. This approach leverages the story's findings as-is, which is the intended use of Einstein Discovery insights.

Exam trap

Salesforce often tests the misconception that administrators can directly edit or retrain Einstein Discovery models to suit specific needs, when in fact the platform is designed to be used as-is, with actionable insights derived from segmenting the data rather than altering the model.

How to eliminate wrong answers

Option A is wrong because retraining the model with only two predictors removes other potentially valuable influences and violates the principle of using the model as generated by Einstein Discovery, which automatically selects the most predictive features. Option B is wrong because deleting an influence from the story does not change the underlying model; it only hides the field from the UI, and the prediction still uses 'LastDonationAmount' internally, so this does not make the output more actionable. Option C is wrong because Einstein Discovery does not allow manual adjustment of influence weights in story settings; the influence percentages are determined by the model's algorithm and cannot be overridden by an administrator.

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20

MCQmedium

A mid-size company uses Sales Cloud with Einstein Lead Scoring and Einstein Activity Capture. The sales team reports that lead scores are not updating for leads that have been engaged via email and calendar events over the past two weeks. The admin checks the Einstein Lead Scoring model and finds that the model status is 'Active' and was retrained last month. The admin also verifies that Einstein Activity Capture is enabled and syncing data correctly. However, the lead scores remain unchanged. Upon further investigation, the admin discovers that the leads were created before the Einstein Lead Scoring model was activated, and the model's training data includes only leads created after activation. The company has over 10,000 leads, but only 200 were created after activation. Historical conversion data for leads created before activation is not being used. What should the admin do to ensure lead scores reflect recent engagement?

A.Map the email and event fields to the lead object so that the model can use them

B.Add the Activity Count field to the scoring fields list in the model configuration

C.Re-enable Einstein Activity Capture to resync all historical emails and events

D.Retrain the Einstein Lead Scoring model using all historical lead data, including pre-activation leads

AnswerD

Retraining with a larger dataset improves the model's ability to score older leads.

Why this answer

Option D is correct because retraining the model with all historical lead data (including pre-activation leads) will include conversion patterns from a larger dataset, improving accuracy and enabling scores for older leads. Option A is wrong because field mapping alone does not cause scoring to update. Option B is wrong because Einstein Activity Capture is already syncing; the issue is with the model.

Option C is wrong because the scoring fields are separate from activity tracking.

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21

Multi-Selecteasy

Which TWO statements are true about Einstein Prediction Builder in Salesforce? (Choose two.)

Select 2 answers

A.It is only available for lead scoring models.

B.It only supports predictions on the Opportunity object.

C.The prediction model automatically retrains every 24 hours.

D.It can use related object fields as predictors in the model.

E.It allows users to create custom predictions using fields from standard and custom objects.

AnswersD, E

Related object fields can be included as input features.

Why this answer

Option D is correct because Einstein Prediction Builder can include fields from related objects (e.g., child objects or lookup objects) as predictors in the model, enabling richer data inputs for predictions. This is achieved through the platform's ability to traverse relationships and aggregate data from related records, which significantly enhances model accuracy.

Exam trap

The trap here is that candidates often assume Einstein Prediction Builder is limited to lead scoring or a single object, but Salesforce designed it to be object-agnostic, and the automatic retraining interval is not 24 hours but rather triggered by data changes or a configurable schedule.

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22

MCQhard

A service team uses Einstein Discovery to analyze customer churn. The story shows 'Average Resolution Time' is a key driver. What is the best action?

A.Reduce Average Resolution Time through process changes

B.Update account records via process builder

C.Configure a flow to send churn alerts

D.Create a custom report on churn

AnswerA

Directly addresses the driver identified by AI.

Why this answer

Einstein Discovery identifies drivers; reducing resolution time directly addresses root cause. Custom reports just show data, flows send alerts, processes update records but don't reduce time.

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23

Multi-Selecteasy

Which TWO features are part of Einstein AI capabilities in Salesforce Sales Cloud?

Select 2 answers

A.Einstein Opportunity Scoring

B.Einstein Case Classification

C.Einstein Lead Scoring

D.Einstein Bots

E.Einstein Article Recommendations

AnswersA, C

Part of Sales Cloud Einstein.

Why this answer

Einstein Opportunity Scoring is a core Einstein AI capability in Sales Cloud that uses predictive models to analyze historical data and assign a score to each opportunity, indicating its likelihood to close. This helps sales reps prioritize their efforts on deals most likely to convert, directly leveraging AI to enhance sales productivity.

Exam trap

Salesforce often tests the distinction between Sales Cloud and Service Cloud Einstein features, so the trap here is assuming that all Einstein AI capabilities are available across all clouds, when in fact features like Case Classification and Article Recommendations are exclusive to Service Cloud.

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24

MCQhard

A Salesforce admin notices that Einstein Account Scoring is not generating scores for all accounts. Some accounts have no score even though they meet the data requirements. What is the most likely cause?

A.The org has fewer than 100 account records

B.Einstein features are not enabled in the org

C.Users do not have the 'View Scores' permission

D.The accounts lack custom fields for scoring

AnswerA

Einstein requires a minimum data volume (e.g., 100 records) to generate scores.

Why this answer

Einstein Account Scoring requires a minimum of 100 account records to generate meaningful scores. If the org has fewer than 100 accounts, the model cannot establish a baseline for scoring, resulting in no scores for any account. This is a hard threshold enforced by the Einstein AI engine, not a configurable setting.

Exam trap

The trap here is that candidates often assume missing scores are due to permission issues or missing custom fields, but the real constraint is a hard data volume minimum that the Einstein engine enforces silently.

How to eliminate wrong answers

Option B is wrong because if Einstein features were not enabled, no Einstein functionality would work at all, not just scoring for some accounts. Option C is wrong because the 'View Scores' permission controls user visibility of scores, not the generation of scores by the AI engine. Option D is wrong because Einstein Account Scoring uses standard fields and does not require custom fields; it leverages existing data like industry, revenue, and engagement history.

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25

MCQhard

An admin reviews the Einstein service configuration JSON. Based on the exhibit, which statement is true?

A.Lead scoring is disabled for the org

B.Opportunities will not have Einstein scores

C.The lead scoring model retrains daily

D.The admin has not configured any scoring fields for leads

AnswerB

Opportunity scoring is disabled (false).

Why this answer

Option B is correct because the JSON exhibit shows that the Einstein Opportunity Scoring feature is disabled (the 'status' field is set to 'Disabled'), meaning opportunities will not have Einstein scores. This is determined by the configuration flag that controls whether Einstein scoring is active for opportunities in the org.

Exam trap

Salesforce often tests the distinction between lead scoring and opportunity scoring configurations, and candidates may mistakenly assume that if lead scoring is enabled, opportunity scoring must also be enabled, or overlook the specific 'status' field for each object in the JSON.

How to eliminate wrong answers

Option A is wrong because the JSON shows lead scoring is enabled (the 'leadScoring' object has 'status' set to 'Enabled'), so lead scoring is not disabled for the org. Option C is wrong because the JSON does not contain any field indicating a retraining schedule for the lead scoring model; the retraining frequency is not specified in this configuration snippet. Option D is wrong because the JSON includes a 'scoringFields' array under 'leadScoring' that lists specific fields like 'Industry' and 'AnnualRevenue', indicating the admin has configured scoring fields for leads.

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26

MCQhard

A Salesforce admin has enabled Einstein Opportunity Scoring but notices that some opportunities are not being scored. The admin checks the data and finds that the opportunities have all required fields. What could be another reason for missing scores?

A.Opportunity team members have not been assigned

B.The org is on a Professional Edition

C.The org has fewer than 100 opportunities with activities

D.The opportunities are missing custom fields

AnswerC

Einstein requires sufficient data history to generate scores.

Why this answer

Einstein Opportunity Scoring requires a minimum of 100 opportunities with activities (e.g., emails, events, tasks) in the last 90 days to generate scores. Even if all required fields are present, the model needs sufficient historical data to calculate a predictive score. Option C correctly identifies this data volume threshold as the likely cause.

Exam trap

The trap here is that candidates assume missing scores are always due to missing fields or permissions, but Salesforce specifically requires a minimum data volume (100 opportunities with activities) for Einstein models to function.

How to eliminate wrong answers

Option A is wrong because opportunity team members are not a prerequisite for Einstein Opportunity Scoring; the model scores opportunities based on field values and activities, not team assignments. Option B is wrong because Einstein Opportunity Scoring is available on Professional Edition with the required add-on licenses, so edition alone does not prevent scoring. Option D is wrong because the admin already confirmed all required fields are present, and missing custom fields would not block scoring unless they are specifically mapped as required by the model, which is not the case here.

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27

MCQmedium

Refer to the exhibit. An admin built a prediction model for case closure within 24 hours. The model accuracy is 72% with 500 training records. Which change would most likely improve accuracy?

A.Change the outcome to 'Escalated'

B.Increase the training sample size to 5000 records

C.Remove the 'Subject' field from the model

D.Add more fields like 'Comments'

AnswerB

More data typically improves accuracy.

Why this answer

Increasing the training sample size from 500 to 5000 records provides the model with more data to learn patterns from, which reduces overfitting and improves generalization. In CRM AI models, larger datasets typically lead to higher accuracy because the algorithm can better capture underlying relationships without being skewed by noise in a small sample.

Exam trap

Salesforce often tests the misconception that adding more fields always improves accuracy, when in reality, irrelevant or noisy features can degrade performance, while increasing sample size is a more reliable method to boost model accuracy.

How to eliminate wrong answers

Option A is wrong because changing the outcome to 'Escalated' alters the target variable entirely, which would require retraining a new model for a different prediction task and does not address the accuracy of the original case closure model. Option C is wrong because removing the 'Subject' field may discard valuable textual features that could help predict closure time; unless the field is irrelevant or noisy, reducing features often harms model performance. Option D is wrong because adding more fields like 'Comments' without ensuring data quality or relevance can introduce noise and increase dimensionality, potentially degrading accuracy rather than improving it.

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28

MCQmedium

A company wants to use Einstein Bots to handle common customer service inquiries. Which feature should be enabled to allow the bot to escalate to a live agent when it cannot resolve the issue?

A.Einstein Case Classification

B.Einstein Reply Recommendations

C.Omni-Channel Flow

D.Einstein Article Recommendations

AnswerC

Omni-Channel Flow can route unresolved bot conversations to live agents.

Why this answer

Option D is correct because Omni-Channel Flow routes work to agents. Option A is wrong because Einstein Case Classification categorizes cases, not escalates. Option B is wrong because Einstein Article Recommendations suggests knowledge articles.

Option C is wrong because Einstein Reply Recommendations suggests responses, not escalation.

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29

MCQeasy

A company wants to use AI to automatically recommend the best next action for service agents during a live chat with a customer. Which Einstein feature should be enabled?

A.Einstein Article Recommendations

B.Einstein Bots

C.Einstein Reply Recommendations

D.Einstein Next Best Action

AnswerD

Next Best Action recommends the best action for agents.

Why this answer

Option D, Einstein Next Best Action, is correct because it is specifically designed to recommend the optimal next step for service agents during live interactions. It uses AI to analyze the customer context, conversation history, and business rules to surface the most relevant action, such as offering a discount or escalating the case, directly within the agent's console.

Exam trap

The trap here is that candidates confuse 'Reply Recommendations' (text suggestions) with 'Next Best Action' (broader business actions), as both appear in the Service Console but serve fundamentally different purposes.

How to eliminate wrong answers

Option A is wrong because Einstein Article Recommendations suggests knowledge base articles for agents or customers to read, not actionable next steps during a live chat. Option B is wrong because Einstein Bots are automated chatbots that handle customer interactions without a human agent, not a feature that recommends actions to a live agent. Option C is wrong because Einstein Reply Recommendations suggests pre-written text responses for agents to send in chat, which is a subset of communication assistance, not a broader recommendation of business actions like transfers or promotions.

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30

MCQhard

An admin notices that Einstein Opportunity Scoring is not generating scores for new opportunities created in the past week. Which troubleshooting step should the admin take first?

A.Retrain the Opportunity Scoring model

B.Verify that users have the 'View Einstein Scores' permission

C.Check that there are at least 50 won and 50 lost opportunities with populated fields

D.Wait 48 hours for the model to update

AnswerC

Einstein models require a minimum of 50 won and 50 lost records to generate scores.

Why this answer

Option C is correct because Einstein Opportunity Scoring requires a minimum of 50 won and 50 lost opportunities with populated fields to generate scores. Without this historical data, the model cannot learn patterns to score new opportunities. The admin should first verify this prerequisite before considering other steps.

Exam trap

Salesforce often tests the prerequisite data requirements for Einstein features, and the trap here is that candidates assume retraining or permissions are the issue, overlooking the minimum data threshold that must be met before scoring can begin.

How to eliminate wrong answers

Option A is wrong because retraining the model is not the first step; the issue is likely a lack of sufficient training data, not a model malfunction. Option B is wrong because the 'View Einstein Scores' permission controls visibility of scores, not the generation of scores; scores are generated regardless of user permissions. Option D is wrong because waiting 48 hours is unnecessary; the model updates daily, but the root cause is insufficient historical data, not a delay in processing.

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31

MCQmedium

A company uses Einstein Lead Scoring and finds that leads with high scores are not converting. What should the admin do to improve prediction accuracy?

A.Increase the scoring model's maximum score

B.Retrain the model with more recent conversion data

C.Disable field-level security for scoring fields

D.Lower the lead conversion threshold

AnswerB

Retraining with current data improves the model's relevance.

Why this answer

Option B is correct because retraining the model with more recent conversion data allows the Einstein Lead Scoring model to adapt to changing patterns in lead behavior and conversion criteria. When high-scoring leads fail to convert, it indicates that the historical data used to train the model no longer reflects current conversion dynamics, so refreshing the training dataset improves prediction accuracy by aligning the model with recent outcomes.

Exam trap

Salesforce often tests the misconception that adjusting score thresholds or model parameters can fix accuracy issues, when the real solution is to refresh the training data to reflect current conversion patterns.

How to eliminate wrong answers

Option A is wrong because increasing the maximum score does not address the underlying data mismatch; it merely scales the output range without improving the model's ability to distinguish converters from non-converters. Option C is wrong because disabling field-level security for scoring fields would expose sensitive data and violate Salesforce security best practices, and it does not affect the model's training data or prediction logic. Option D is wrong because lowering the lead conversion threshold artificially inflates conversion rates without fixing the model's accuracy; it treats the symptom (low conversion) rather than the cause (stale training data).

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32

Multi-Selecteasy

Which THREE of the following are Einstein features available for Sales Cloud? (Choose three.)

Select 3 answers

A.Einstein Lead Scoring

B.Einstein Opportunity Scoring

C.Einstein Activity Capture

D.Einstein Bot

E.Einstein Analytics

AnswersA, B, C

Predicts lead conversion.

Why this answer

Einstein Lead Scoring is a correct Einstein feature for Sales Cloud because it uses predictive AI models to automatically score leads based on historical conversion data, helping sales reps prioritize high-quality leads. It is natively integrated into Sales Cloud without requiring additional licenses or complex setup, leveraging standard Salesforce objects and fields.

Exam trap

Salesforce often tests candidates' ability to distinguish between Einstein features that are native to Sales Cloud (like scoring and activity capture) versus those that are cross-cloud or require separate licenses (like Einstein Bot or Einstein Analytics), leading to confusion when options include features that are technically available but not part of the core Sales Cloud Einstein set.

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33

MCQmedium

A marketing team wants to predict which segment of customers will likely purchase a new product. Which Einstein feature is most appropriate?

A.Custom Reports

B.List Views

C.Campaigns

D.Einstein Segment Prediction

AnswerD

Predicts segment behavior using ML.

Why this answer

Einstein Segment Prediction uses predictive modeling and machine learning to analyze historical customer data and identify which segments are most likely to purchase a new product. It is specifically designed for predictive segmentation based on behavioral and demographic patterns, making it the most appropriate choice for this use case.

Exam trap

Salesforce often tests the distinction between descriptive analytics (reports, list views) and predictive analytics (Einstein features), leading candidates to choose a familiar but incorrect option like Custom Reports or List Views instead of the AI-powered prediction tool.

How to eliminate wrong answers

Option A is wrong because Custom Reports are used for creating ad-hoc data views and analyzing past performance, not for generating predictive insights about future purchase likelihood. Option B is wrong because List Views are static filters that display records based on predefined criteria, lacking any machine learning or predictive capability. Option C is wrong because Campaigns are used to manage marketing outreach and track engagement, not to predict which customer segments will purchase a product.

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34

MCQhard

Refer to the exhibit. An admin runs two queries on the Lead object. Both include the custom field Score__c used by Einstein Lead Scoring. The second query is significantly slower. What is the most likely cause?

A.The query is not using a selective filter on Score__c

B.The query uses a date function that is not selective

C.The Score__c field is indexed

D.The Einstein Lead Scoring job is running simultaneously

AnswerA

Large result set without index on Score__c causes slow performance.

Why this answer

Option A is correct because Einstein Lead Scoring uses the Score__c field to store lead scores, and the field is not indexed by default. When a query filters on Score__c without an index, it forces a full table scan on the Lead object, which becomes significantly slower as the number of leads grows. The second query likely includes a non-selective filter on Score__c (e.g., a range or inequality), which cannot leverage any existing index and thus degrades performance.

Exam trap

Salesforce often tests the misconception that Einstein features automatically index their underlying fields, when in fact custom fields like Score__c are not indexed by default and require manual indexing for query performance.

How to eliminate wrong answers

Option B is wrong because the question explicitly states both queries include the custom field Score__c, and there is no mention of a date function in either query; the performance difference is due to the filter on Score__c, not a date function. Option C is wrong because Score__c is a custom field used by Einstein Lead Scoring and is not automatically indexed; if it were indexed, the second query would likely be faster, not slower. Option D is wrong because Einstein Lead Scoring jobs run asynchronously in the background and do not directly impact the performance of individual SOQL queries; the slowdown is due to query selectivity, not concurrent job execution.

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35

MCQeasy

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

A.It predicts a numeric value

B.It predicts the value of a text field

C.It is currently retraining the model

D.It predicts a binary outcome (e.g., convert or not)

AnswerD

Binary classification yields two possible results.

Why this answer

Option D is correct because Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

Exam trap

Salesforce often tests the distinction between regression (numeric prediction) and classification (binary outcome) by showing a JSON with a 'probability' field, leading candidates to mistakenly think it predicts a numeric value when the 'predictedValue' field clearly indicates a categorical label.

How to eliminate wrong answers

Option A is wrong because predicting a numeric value (regression) would produce a JSON with a single numeric 'value' field and no categorical 'predictedValue' or 'probability' fields, whereas the exhibit shows a 'predictedValue' with a string label. Option B is wrong because predicting a text field (e.g., a free-form string) would require a different model type (like Einstein Text Prediction) and the JSON would contain a 'text' or 'value' field, not a binary 'predictedValue' with two possible states. Option C is wrong because the JSON does not include any retraining status indicators such as 'retrainingInProgress: true' or 'modelStatus: retraining'; the model is deployed and serving predictions, as shown by the presence of a score.

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36

MCQhard

A company uses Einstein Prediction Builder to predict whether a lead will convert. The model's confidence score is low, and the admin wants to improve accuracy. What is the most effective action?

A.Create multiple prediction models and average their scores

B.Add more custom fields as predictor fields

C.Increase the amount of historical data for training

D.Increase the frequency of model retraining

AnswerC

More data generally improves model accuracy.

Why this answer

Option B is correct because increasing the amount of high-quality historical data provides more examples for the model to learn from, improving accuracy. Option A is incorrect because more features can improve accuracy but only if they are relevant; just adding any field may cause noise. Option C is incorrect because Einstein models train automatically on your data, and you cannot directly increase training frequency.

Option D is incorrect because training multiple models is not supported and doesn't improve the single model.

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37

Multi-Selecthard

Which THREE factors influence the prediction accuracy of Einstein Lead Scoring?

Select 3 answers

A.Custom formula fields on the lead object

B.Number of times a lead is viewed by sales reps

C.Historical conversion data of leads

D.Conversion patterns across different lead sources

E.Values in standard lead fields like industry and company size

AnswersC, D, E

The model learns from past conversions.

Why this answer

Option C is correct because Einstein Lead Scoring relies on historical conversion data to identify patterns that distinguish leads likely to convert. By analyzing past leads that converted, the model learns which attributes and behaviors correlate with successful outcomes, directly influencing prediction accuracy.

Exam trap

Salesforce often tests the misconception that any lead-related data, such as custom formula fields or rep viewing counts, directly influences Einstein Lead Scoring, when in fact only historical conversion data and patterns from standard fields like industry and lead source are used.

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38

Multi-Selecteasy

Which TWO actions can be performed using Einstein Activity Capture?

Select 2 answers

A.Automatically log emails from Outlook or Gmail

B.Update opportunity amounts based on email content

C.Create tasks from email attachments

D.Generate leads from email signatures

E.Automatically log meetings from calendar events

AnswersA, E

Einstein Activity Capture syncs emails to Salesforce records.

Why this answer

Einstein Activity Capture is designed to automatically log user activities such as emails and meetings from connected email and calendar systems (Outlook, Gmail, Exchange) into Salesforce records. Option A is correct because the feature captures emails sent or received from these providers and logs them as EmailMessage records against the relevant contacts, leads, and opportunities without manual user intervention.

Exam trap

Salesforce often tests the misconception that Einstein Activity Capture can perform advanced data extraction or automation tasks (like updating fields or creating records from email content), when in reality it is limited to logging existing email and calendar events.

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39

MCQeasy

A manufacturing company uses Salesforce Sales Cloud with Einstein Activity Capture to log emails and events automatically. The sales team has noticed that some emails from Microsoft Outlook are not appearing in the Einstein Activity Capture dashboard, even though the users have configured the connection correctly. The administrator checks the activity capture settings and sees that the data source is enabled and users have the correct permissions. The missing emails are from a specific domain, @supplier.com, which is a known vendor. What should the administrator do to ensure these emails are captured?

A.Increase the sync frequency for Einstein Activity Capture to every 5 minutes.

B.Add the @supplier.com domain to the allowlist in Einstein Activity Capture settings.

C.Instruct users to manually log the supplier emails as activities in Salesforce.

D.Assign the 'Einstein Activity Capture User' permission set to the supplier's email domain.

AnswerB

Domain allowlists in Activity Capture settings control which external emails are captured.

Why this answer

Einstein Activity Capture uses allowlists and blocklists to control which emails are captured. By default, emails from external domains may be excluded to reduce noise. Adding @supplier.com to the allowlist ensures that emails from that domain are explicitly included in the capture process, overriding any default filtering.

Exam trap

The trap here is that candidates may confuse Einstein Activity Capture's domain filtering with permission sets or sync frequency, overlooking the fact that the allowlist is the specific control for including or excluding emails from particular domains.

How to eliminate wrong answers

Option A is wrong because increasing sync frequency does not address the root cause of emails being filtered out by domain; it only changes how often the system checks for new data. Option C is wrong because manually logging emails bypasses the automation that Einstein Activity Capture is designed to provide, and it is not a scalable solution for ongoing capture. Option D is wrong because the 'Einstein Activity Capture User' permission set is assigned to users, not to email domains; domains cannot be assigned permission sets.

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40

MCQhard

Universal Containers (UC) uses Einstein Lead Scoring to prioritize leads. They have 500,000 leads in the system. Recently, the model scores have been inconsistent: some leads with low activity receive high scores, while active leads score low. The model was trained 3 months ago. UC updates lead records daily via an external system, but the data is often incomplete (e.g., missing company size). Support has reported slow performance on lead views. The admin notices that the 'Data Refresh Status' for Einstein Lead Scoring shows 'Pending' for 2 weeks. UC wants to improve model accuracy and performance. Which action should the admin take first?

A.Schedule a data transform to cleanse and refresh lead data before model training

B.Increase data retention period for lead records

C.Rebuild the Einstein Lead Scoring model using different fields

D.Increase the model training frequency to weekly

AnswerA

Ensures the model uses accurate, up-to-date data.

Why this answer

Option A is correct because the 'Data Refresh Status' has been 'Pending' for two weeks, indicating that the model is not receiving updated lead data. Scheduling a data transform to cleanse and refresh lead data before model training directly addresses the root cause: incomplete and stale data (e.g., missing company size) leads to inconsistent scores. This action ensures the model trains on clean, current data, improving both accuracy and performance.

Exam trap

The trap here is that candidates may focus on model retraining (Option C or D) without realizing that the core issue is a stalled data refresh, not the model configuration or frequency.

How to eliminate wrong answers

Option B is wrong because increasing the data retention period does not fix the immediate issue of stale or incomplete data; it only keeps older records longer without addressing data quality or refresh delays. Option C is wrong because rebuilding the model with different fields does not resolve the underlying data refresh problem; the model will still train on incomplete or outdated data, leading to inconsistent scores. Option D is wrong because increasing training frequency to weekly does not help if the data itself is not being refreshed; the model would simply train more often on the same stale data, perpetuating inaccuracies.

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41

MCQhard

Refer to the exhibit. A developer wrote a trigger to call an Einstein prediction API on lead insert. When new leads are created, the trigger fails with a 'Too many SOQL queries' error. What is the most likely cause?

A.The trigger is executed before the lead is saved

B.The trigger is querying a related object without an index

C.The trigger is making API calls in a loop, causing governor limit issues

D.The Score__c field is not writeable

AnswerC

Looping API calls can exceed limits.

Why this answer

Option C is correct because the trigger is making synchronous API calls to the Einstein prediction service inside a loop that iterates over each new lead. Each API call counts against the 'Number of callouts' governor limit (default 100 per transaction), and if more leads are inserted than the limit allows, the transaction fails with a 'Too many SOQL queries' error—though the error message is misleading, the root cause is exceeding the callout limit, not SOQL queries. The trigger should batch the API calls or use asynchronous processing to stay within limits.

Exam trap

Salesforce often tests the misconception that 'Too many SOQL queries' always means too many SOQL queries, but in this context it actually masks a callout limit issue—candidates may overlook the fact that API calls count against a different governor limit that produces the same error message.

How to eliminate wrong answers

Option A is wrong because the timing of trigger execution (before vs. after save) does not cause a 'Too many SOQL queries' error; that error is a governor limit issue, not a save-order issue. Option B is wrong because querying a related object without an index would cause a 'Too many rows' or performance warning, not a 'Too many SOQL queries' error—the error specifically indicates the number of SOQL queries (or callouts) exceeded the limit, not a missing index. Option D is wrong because the Score__c field being non-writeable would produce a field-level validation or DML exception, not a governor limit error like 'Too many SOQL queries'.

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42

Multi-Selecthard

Which TWO are best practices when implementing Einstein Recommendation Builder?

Select 2 answers

A.Use a dataset that reflects recent customer interactions

B.Disable negative feedback to simplify the model

C.Include all available data, even if it is old or unrelated

D.Use a single dataset that contains the entire history of the org

E.Regularly review and test the recommendation model

AnswersA, E

Recent data provides timely recommendations.

Why this answer

Option A is correct because Einstein Recommendation Builder relies on recent customer interaction data to generate accurate and relevant product or content recommendations. Using stale or outdated data can lead to irrelevant suggestions, as the model learns from historical patterns that may no longer reflect current customer preferences or behaviors.

Exam trap

Salesforce often tests the misconception that 'more data is always better' or that 'simplifying the model by disabling feedback improves performance,' when in reality, data quality and feedback signals are critical for accurate recommendations.

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43

MCQeasy

A sales rep wants to use Einstein Activity Capture to automatically log emails and meetings. Which prerequisite must be met?

A.Chatter must be disabled for the organization

B.Sales Cloud Einstein licenses for all users

C.The feature is automatically enabled once email integration is configured

D.Users must grant access to their email and calendar via OAuth

AnswerD

Users must authorize Salesforce to access their email and calendar.

Why this answer

Einstein Activity Capture requires users to explicitly grant access to their email and calendar via OAuth (Option D). This OAuth-based authentication allows Salesforce to securely sync emails and meetings from the user's email provider (e.g., Gmail, Outlook) into Salesforce records. Without this explicit consent, the feature cannot access or log the user's activity data.

Exam trap

Salesforce often tests the misconception that Einstein Activity Capture is automatically enabled or requires a separate Einstein license, when in reality it requires explicit user-level OAuth consent and is available with standard Salesforce licenses.

How to eliminate wrong answers

Option A is wrong because Chatter does not need to be disabled for Einstein Activity Capture to work; in fact, Chatter can remain enabled and is unrelated to the email/calendar sync process. Option B is wrong because Sales Cloud Einstein licenses are not a prerequisite for Einstein Activity Capture; the feature is available with standard Salesforce licenses and does not require an add-on Einstein license. Option C is wrong because Einstein Activity Capture is not automatically enabled when email integration is configured; it requires explicit setup, including OAuth authorization from each user, and is not a default consequence of email integration.

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